{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f188e6f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>时间</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0:00</td>\n",
       "      <td>1.010065</td>\n",
       "      <td>1.015373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0:05</td>\n",
       "      <td>1.007142</td>\n",
       "      <td>1.005767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0:10</td>\n",
       "      <td>1.010765</td>\n",
       "      <td>1.005684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0:15</td>\n",
       "      <td>1.008393</td>\n",
       "      <td>1.008145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0:20</td>\n",
       "      <td>1.004085</td>\n",
       "      <td>1.015046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>23:35</td>\n",
       "      <td>1.810196</td>\n",
       "      <td>1.295716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>23:40</td>\n",
       "      <td>1.710392</td>\n",
       "      <td>1.094984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>23:45</td>\n",
       "      <td>1.038173</td>\n",
       "      <td>1.134390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>23:50</td>\n",
       "      <td>1.352387</td>\n",
       "      <td>1.132372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>23:55</td>\n",
       "      <td>1.395330</td>\n",
       "      <td>1.275665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        时间         x         y\n",
       "0     0:00  1.010065  1.015373\n",
       "1     0:05  1.007142  1.005767\n",
       "2     0:10  1.010765  1.005684\n",
       "3     0:15  1.008393  1.008145\n",
       "4     0:20  1.004085  1.015046\n",
       "..     ...       ...       ...\n",
       "283  23:35  1.810196  1.295716\n",
       "284  23:40  1.710392  1.094984\n",
       "285  23:45  1.038173  1.134390\n",
       "286  23:50  1.352387  1.132372\n",
       "287  23:55  1.395330  1.275665\n",
       "\n",
       "[288 rows x 3 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "\n",
    "data = pandas.read_csv('地理位置.csv')\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13913c96",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = 'Microsoft Yahei'\n",
    "plt.rcParams['figure.figsize'] = (16,9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4aa181d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='x', ylabel='y'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2AACdYIsvAAAAtSCgAgAAUAsCKgAAALUgoAIAAFALAioAAAC1IKACAABQCwIqAAAAtSCgAgAAUAsCKgAAALUgoAIAAFALAioAAAC1IKACAABQCwIqAAAAtSCgAgAAUAsCKgAAALUgoAIAAFALAioAAAC1IKACAABQCwIqAAAAtSCgAgAAUAsCKgAAALUgoAIAAFALAioAAAC1IKACAABQCwIqAAAAtSCgAgAAUAtzO10AAJwL7XaV7YND2b2/laU9zfT1dqfRKJ0uCwCYgCkJqKWUhUl+PcnaJK0kL6qq6shUXBuAma/drnLHtl3ZuHlrWsPtNLsa2bR+TdatXiakAsA0MlVbfH81yVeSXJpkdZLhKbouALPAtx4bGg2nSdIabmfj5q351mNDHa4MAJiISQ+opZRlSb43yQeq41pVVVWTfV0AZo8djw+NhtMRreF2Hn5cQAWA6WQqVlBXJ/lWkttLKd8opfxKKWXMfqtSyo2llC2llC179+6dgpIAmEm6z5ubZtfY/6Q1uxq54DytFgBgOpmKgLokyYuTvCPJy5J8X5LXnviEqqpurapqoKqqgcWLF09BSQDMJEt7zs9NN6wcDanNrkZuumFllvac3+HKAICJmIqvlvckuaeqqkeSpJTyl0leOAXXBWCWWLGoOyuXzs+N1/WnXSWNkqxcOj8rFnV3ujQAYAKmIqDeneTXSynPSzKY5AeS/PspuC4As0SjUXL9C5em/+L52XOglWU9zRxrJ1/81qCRMwAwjUx6QK2qaqiU8o4kf5nk/CS/XVXV5yb7ugDMLo1GSf/i+enr7TZyBgCmqSkZM1NV1Z9VVbW6qqorqqp6/1RcE4DZafvg+CNntg/q6AsAdTdVc1ABYErs3t8ad+TMngOtDlUEAJwuARWAGWVpT3PckTNLFjQ7VBEAcLoEVABmlL7e7mxav2bMyJlN69ekr1dHXwCoOxPMAZhRGo2SdauXZdWGtdlzoJUlC3TxBYDpQkAFYMYZ6ejbv3h+p0sBACbAFl8AAABqQUAFAACgFgRUAAAAakFABQAAoBYEVAAAAGpBQAUAAKAWBFQAAABqQUAFAACgFgRUAAAAakFABQAAoBYEVAAAAGpBQAUAAKAWBFQAAABqQUAFAACgFgRUAAAAakFABQAAoBYEVAAAAGpBQAUAAKAWBFQAAABqQUAFAACgFuZ2ugAAmM6OHm1n28592bmvleUL52X18p7MnTv2+992u8r2waHs3t/K0p5m+nq702iUDlUMAPUloALAGTp6tJ1PfvnRvPuTX0lruJ1mVyPvf91Ved1LLhkNqe12lTu27crGzVtHn7Np/ZqsW71MSAWAZ7DFFwDO0Lad+0bDaZK0htt59ye/km07940+Z/vg0Gg4HXnOxs1bs31wqCM1A0CdCagAcIZ27muNBs8RreF2du1rjf559/7xn7PnQCsAwFgCKgCcoeUL56XZNfY/pc2uRpYtbI7+eWlPc9znLFnQDAAwloAKAGdo9fKevP91V40G0JEzqKuXLxx9Tl9vdzatXzPmOZvWr0lfb3dHagaAOitVVXW6hjEGBgaqLVu2dLoMADgtI118d+1rZdnCZlYvX3jKLr57DrSyZIEuvgDMbqWUe6qqGhjvMV18AeAszJ3byEuef1Fe8vxTP6fRKOlfPD/9i+dPXWEAMA3Z4gsAAEAtCKgAAADUgoAKAABALQioAAAA1IKACgAAQC0IqAAAANSCMTPApBiZ+7h7fytLe8x9hKni7x4A05mACpxz7XaVO7btysbNW9MabqfZ1cim9WuybvUyH5RhEtXl756QDMCZssUXOOe2Dw6NfkBOktZwOxs3b832waEOVwYz21T93Wu3qzy092D+9sHH8tDeg2m3qzGP3bFtV159y1358d/4Yl59y125Y9uuMc8BgFMRUIFzbvf+1ugH5BGt4Xb2HGh1qCKYHabi795zBVBfUAFwNgRU4Jxb2tNMs2vs7aXZ1ciSBc0OVQSzw1T83XuuAOoLKgDOhoAKnHN9vd3ZtH7N6AflkXNwfb3dHa4MZrap+Lv3XAHUF1QAnA1NkoBzrtEoWbd6WVZtWJs9B1pZskCTFJgKU/F3bySAnhhSTwygIyH5mY2afEEFwOkoVVWvpgUDAwPVli1bOl0GADCO0+kUPNLF1xdUAIynlHJPVVUD4z4moAIAE3H0aDvbdu7Lzn2tLF84L6uX92TuXKeGADg9zxZQbfEFAE5bu13lL762e8wK6gffeE1++KrlQioAZ81/SQCA0zZeF9933n5v/tdDg2adAnDWBFQA4LSdqovvlh2Pm3UKwFkTUAGA03aqMTLH2smOwSGrqACcFQEVADhtfb3d+eAbrxkza/Xn163K/PPnZM+Bw/mfDzyWo0fbz/EqADA+TZIAgNPWaJT88FXLc9EF52XLjsczr2tODh9r5w/+/uG85ppL8uiTT6VdVfm+yy/WNAmACRNQAYAJmTu3kVdecXHOn9vIngOH8yt/8fW8eWBFbrnz/jGdfV97zfPMPwVgQny1CQBMWKNRsnjB+bl/z4G85ppLRsNp8p3OvpomATBRAioAcEb6ervz8ssWZU4j43b23XOg1aHKAJiubPEFAM5Io1HyPf29aVdVml2NMSG12dXIkgXNDlYHwHRkBRUAOGNz5zbyfZdffFJn303r16Svt7vD1QEw3VhBBQAmpN2usn1wKLv3t7J8YTPH2snzLmzmYzdem0NHjmVpTzN9vd0aJAEwYVMSUEsp25Kc//QfP19V1c9OxXUBYKY6erSdbTv3Zee+VpYvnJfVy3umZKzLkSPHcs+3n8jDjx9KT3Nuvv34UN7zx18d7d67af2afPcLeoVTAM7IVK2gnl9V1RVTdC0AmNGOHm3nk19+NO/+5FdGg+H7X3dVXveSSyY1pB492s6n7v3H3Pyp71z3F167OlcumZ97H92f1nA7GzdvzaoNa9O/eP6k1QHAzDVVZ1CrKboOAMx423buGw2nyfGOue/+5Feybee+Sb/uSDgdue5//JNtedt1l48+R/deAM7GpAfUUkp3kqWllIdKKZ8rpbx8nOfcWErZUkrZsnfv3skuCQCmtZ37WuOOddm1b3KD4amu+9SRo6N/1r0XgLMx6QG1qqqhqqp6qqrqT/JrST4xznNurapqoKqqgcWLF092SQAwrS1fOG+0Y+6IZlcjyxZObjA81XW7z5s7+s+69wJwNqZ0zExVVX+YpFlKuXAqrwsAM8nq5T15/+uuGjPW5f2vuyqrly+c8uu+70euylWX9OQPbvzufGbD2qxbvUyDJADO2KQ3SSqlLEwyt6qqwVLKDyV5vKqqJyf7ugAwU82d28jrXnJJVi6Zn137Wlm2sJnVyxdOehffZ7tu38WaIgFw9qaii++iJH9VSkmSXUneNAXXBIAZbe7cRl7y/IvykufPjusCMDtMekCtqupbSS5/zicCAAAwq03pGVQAAAA4FQEVAACAWhBQAQAAqAUBFQAAgFoQUAEAAKgFARUAAIBaEFABAACoBQEVAACAWhBQAQAAqAUBFQAAgFoQUAEAAKgFARUAAIBaEFABAACoBQEVAACAWhBQAQAAqAUBFQAAgFoQUAEAAKgFARUAAIBamNvpAgAAJlu7XWX74FB2729laU8zfb3daTRKp8sC4BkEVABgRmu3q9yxbVc2bt6a1nA7za5GNq1fk3WrlwmpADVjiy8AMKNtHxwaDadJ0hpuZ+Pmrdk+ONThygB4JgEVAJjRdu9vjYbTEa3hdvYcaHWoIgBORUAFAGa0pT3NNLvGfuRpdjWyZEGzQxUBcCoCKgAwo/X1dmfT+jWjIXXkDGpfb3eHKwPgmTRJAgBmtEajZN3qZVm1YW32HGhlyQJdfAHqSkAFAGa8RqOkf/H89C+e3+lSAHgWtvgCAABQCwIqAAAAtSCgAgAAUAsCKgAAALUgoAIAAFALAioAAAC1IKACAABQCwIqAAAAtSCgAgAAUAsCKgAAALUgoAIAAFALAioAAAC1IKACAABQCwIqAAAAtSCgAgAAUAsCKgAAALUgoAIAAFALAioAAAC1IKACAABQCwIqAAAAtXBaAbWUcvFkFwIAAMDsdrorqH9fSvmzUso/L6UsmNSKAAAAmJVOK6BWVfWCJO9JsirJ50spHy+lvKGUcv6kVgcAAMCscdpnUKuq+vuqqt6V5GeSHE7ye0m+UUr5tVLKokmqDwAAgFnidM+g/kAp5VdLKfcn+aUkf55kWZIrk3wlyZ9NXokAAADMBnNP83m/kOT3k7y3qqq9z3js10op/+RcFgUAAMDsc1oBtaqqtc/x+PpzUw4AAACz1emuoALPot2usn1wKLv3t7K0p5m+3u40GqXTZQEAwLQioMJZarer3LFtVzZu3prWcDvNrkY2rV+TdauXCakAADABp93FFxjf9sGh0XCaJK3hdjZu3prtg0MdrgwAAKYXARXO0u79rdFwOqI13M6eA60OVQQAANOTgApnaWlPM82usX+Vml2NLFnQ7FBFAAAwPQmocJb6eruzaf2a0ZA6cga1r7e7w5UBAMD0okkSnKVGo2Td6mVZtWFt9hxoZckCXXwBAOBMTFlALaV8Jsk/VlX1tqm6JkyVRqOkf/H89C+e3+lSAJghjDADZqMpCaillB9MsibJP07F9QAApjMjzIDZatLPoJZSupP8xyS/PNnXAgCYCYwwA2arqWiS9KEkm5I8eaonlFJuLKVsKaVs2bt37xSUBABQX0aYAbPVpAbUUspPJ6mqqtr8bM+rqurWqqoGqqoaWLx48WSWBABQe0aYAbPVZK+gviPJ95dSvp7kPyX50VLKb03yNQEApjUjzIDZalKbJFVVNTDyz6WUn0nySl18AQCenRFmwGxlDioAQA0ZYQbMRlMWUKuq+u0kvz1V1wMAAGB6mYouvgAAAPCcBFQAAABqQUAFAACgFgRUAAAAakFABQAAoBYEVAAAAGpBQAUAAKAWBFQAAABqQUAFAACgFgRUAAAAakFABQAAoBYEVAAAAGpBQAUAAKAWBFQAAABqQUAFAACgFgRUAAAAakFABQAAoBYEVAAAAGpBQAUAAKAWBFQAAABqQUAFAACgFgRUAAAAakFABQAAoBYEVAAAAGpBQAUAAKAWBFQAAABqYW6nCwAAYGZot6tsHxzK7v2tLO1ppq+3O41G6XRZwDQioAIAcNba7Sp3bNuVjZu3pjXcTrOrkU3r12Td6mVCKnDabPEFAOCsbR8cGg2nSdIabmfj5q3ZPjjU4cqA6URABQDgrO3e3xoNpyNaw+3sOdDqUEXAdCSgAgBw1pb2NNPsGvvRstnVyJIFzQ5VBExHAioAAGetr7c7m9avGQ2pI2dQ+3q7O1wZMJ1okgQAwFlrNErWrV6WVRvWZs+BVpYs0MUXmDgBFQCAc6LRKOlfPD/9i+d3uhRgmrLFFwAAgFoQUAEAAKgFARUAAIBaEFABAACoBQEVAACAWhBQAQAAqAUBFQAAgFoQUAEAAKgFARUAAIBaEFABAACoBQEVAACAWhBQAQAAqAUBFQAAgFqY2+kCYCq021W2Dw5l9/5WlvY009fbnUajdLosAADgBAIqM167XeWObbuycfPWtIbbaXY1smn9mqxbvUxIBQCAGrHFlxlv++DQaDhNktZwOxs3b832waEOVwYAAJxIQGXG272/NRpOR7SG29lzoNWhigAAgPEIqMx4S3uaaXaNfas3uxpZsqDZoYoAAIDxCKjMeH293dm0fs1oSB05g9rX293hygAAgBNpksSM12iUrFu9LKs2rM2eA60sWaCLLwAA1JGAyqzQaJT0L56f/sXzO10KAABwCrb4AgAAUAsCKgAAALUgoAIAAFALAioAAAC1IKACAABQC7r4AgAwa7XbVbYPDmX3/laW9hhFB5026QG1lNJI8udJLktSJdlQVdWfT/Z1AQDg2bTbVe7YtisbN29Na7idZlcjm9avybrVy4RU6JCp2OJbJfnnVVVdmeSmJL84BdcEAIBntX1waDScJklruJ2Nm7dm++BQhyuD2WvSA2p13M6n/3hZki8/8zmllBtLKVtKKVv27t072SUBAEB272+NhtMRreF29hxodagiYEqaJJVSfq6UMpjk3yR57zMfr6rq1qqqBqqqGli8ePFUlAQAwCy3tKeZZtfYj8PNrkaWLGh2qCJgSgJqVVW/XFVVb5J3JfnzUopN/QAAdFRfb3c2rV8zGlJHzqD29XZ3uDKYvaa0i29VVX9USrklSW+Sx6by2gAAcKJGo2Td6mVZtWFt9hxoZckCXXyh06aii29/kkNVVe0qpXxPklZVVcIpAAAd12iU9C+en/7F8ztdCpCpWUG9MMkdpZQ5SfYkefMUXBMAAIBpZtIDalVV/5Dkysm+DgAAANPblDRJAgAAgOcioAIAAFALAioAAAC1IKACAABQCwIqAAAAtSCgAgAAUAsCKgAAALUgoAIAAFALAioAAAC1IKACAABQCwIqAAAAtSCgAgAAUAsCKgAAALUgoAIAAFALAioAAAC1IKACAABQCwIqAAAAtSCgAgAAUAsCKgAAALUgoAIAAFALAioAAAC1IKACAABQCwIqAAAAtSCgAgAAUAsCKgAAALUwt9MFAAAAp6/drrJ9cCi797eytKeZvt7uNBql02XBOSGgAgDANNFuV7lj265s3Lw1reF2ml2NbFq/JutWLxNSmRFs8QUAgGli++DQaDhNktZwOxs3b832waEOVwbnhoAKAADTxO79rdFwOqI13M6eA60OVQTnloAKAADTxNKeZppdYz/CN7saWbKg2aGK4NwSUAEAYJro6+3OpvVrRkPqyBnUvt7uDlcG54YmSQAAME00GiXrVi/Lqg1rs+dAK0sW6OLLzCKgAgDANNJolPQvnp/+xfM7XQqcc7b4AgAAUAsCKgAAALUgoAIAAFALAioAAAC1IKACAABQCwIqAAAAtSCgAgAAUAsCKgAAALUgoAIAAFALAioAAAC1IKACAABQCwIqAAAAtSCgAgAAUAsCKgAAALUgoAIAAFALAioAAAC1IKACAABQCwIqAAAAtSCgAgAAUAtzO10AndduV9k+OJTd+1tZ2tNMX293Go3S6bIAAIBZRkCd5drtKnds25WNm7emNdxOs6uRTevXZN3qZUIqAAAwpWzxneW2Dw6NhtMkaQ23s3Hz1mwfHOpwZQAAwGwjoM5yu/e3RsPpiNZwO3sOtDpUEQAAMFsJqLPc0p5mml1j3wbNrkaWLGh2qCIAAGC2ElBnub7e7mxav2Y0pI6cQe3r7e5wZQAAwGyjSdIs12iUrFu9LKs2rM2eA60sWaCLLwAA0BkCKmk0SvoXz0//4vmdLgUAgBnIWENO16QH1FLKeUn+vyQ/kKQk+XdVVd0+2dcFAAA6z1hDJmIqzqAuSnJnVVVXJvnhJB8tpXRNwXXPuXa7ykN7D+ZvH3wsD+09mHa76nRJAABQa8YaMhGTvoJaVdWuJB9/+p+/WUo5muSCJPsm+9rnkm9+AABg4p5trKEjZjzTlHbxLaW8Jcm9VVXte8bPbyylbCmlbNm7d+9UlnTazvSbH6uuAPXk/gwwNYw1ZCKmrElSKeXfJXlzklc/87Gqqm5NcmuSDAwM1PITwpl882PVFaCe3J8Bps7IWMNn3nONNWQ8UxJQSykfSdKd5Puqqjo0Fdc810a++TkxpD7XNz+nWnVdtWGt7QwAHeT+DDB1jDVkIiZ9i28p5dokL6yq6memazhNvvPNz8j2hNP55ufZVl0B6Bz3Z4CpNTLW8Nr+i9O/eL5wyilNxQrqmiQDpZQHTvjZ26uqumMKrn3OnMk3P2ey6grA5HN/BoB6mvQV1Kqq/mtVVRdWVXXFCf+bVuF0xES/+TmTVdcTaeABMDnO9v4MAEyOUlX1Cj0DAwPVli1bOl3GOdNuV9k+ODTh/fYaeABMrjO9PwMAZ6eUck9VVQPjPiag1tNDew/m1bfcddL2s89o4AEAAExjzxZQp3QOKqdPAw8AAGC2mbI5qEyMBh4AADD7jBxB2b2/laU9s+8IihXUmtLAAwAAZpeRPjSvvuWu/PhvfDGvvuWu3LFt17M2S51pjVWdQa0xDTwAAGD2mGgfmunaWNUZ1GnKQGMAAJiezmRl85l9aJYvbOatr+zPN3cfOOk12u0q9z365Gg4TY73rNm4eWu2Dw6d+3+hKeIMKgAAwDl0piubJ/ahWb6wmZ+69rLccuf9aQ23c1nvvLzvR65O15ySJQua+dbgwdz7yL5TNladrpM/rKBOAzNtXzkAAMxk2weHJrSyOfJ5f/f+Vn7jpwZyWe+8vOFll46G0+ULm3nzwIrceNuW/PhvfDE//Kt35f7dB3PBeXNGe9aMmO6NVa2g1tx03VcOADPZbO+yCTy7ZxsZ+cyVzfE+73/wjddk3gkTPU4MqyOv9aHP3p+3f/8V2XD9ytHHml2NfOD1V6dRjr/udLwvCag1d6pvX1ad4qA09eXDDMDM4Mtj4LlMZGTkeJ/333n7vfnYjd8z+hqlZNzAu3jB+fnvd+/IW1/ZnzmN5MolC/KLn/lanjh0ZNrel2zxrbln+/aF6eNMWoYDUE8T3boHzD4jIyMv652Xf/X9V2TDDVfkN35qICsuuuCk557q8/7wsWMnjZ08UbOrkUeffCrrrlqeT9/7aOZ1zckvfuZr2bmvNa3vS1ZQa24i375QX1bCAWaOiWzdA2anRqPkVS9amuFjx1dDn223xak+7y/qPj8vW7EoqzaszeNDh7Nyyfwxr7Xh+pW57e4deeLQkfzuW16Rmz62NTv3fWcRa7rel6yg1tzIty8nfnOyaf2a9PV2d7gyJsJKOMDMMfJh8kS+PAae6eEnDo0GyuTUuy1WXHRBPvjGa8b9vD8ydnKgrzc/fNXyfPSnB/L266/IW1/Zn9vu3jG6Wjp05GieOHRkzOtO1/uSFdSaazRK1q1ellUb1mbPgVaWLHB2cTqyEg4wc4x8efzMM6i+PAZOdDq7LdrtKn/xtd3Z9JffGD1HOnDZonxvf++Yz/vtdpW/vn9PWkfaxxsgnfCaza5GViyaOfclAXUaGPnmZLotz/MdPswAzBy+PAZOx+ksUHzrsaF88I6v5TXXXJJSknaV3Pyp+/JbP/2KXL7kO5/9v/3EUHbvP5z3ffqrY7b4fmzLw3nnuhflBRd35wUXd8+I+1Kpqno1aRkYGKi2bNnS6TLgnBvp4jvdbxoAADy30+n4fdf9e3LfI/vHjInZcP3KXHNpT165csno6/zN/XvzL3/vnpPC7u++5RUZ6Fs07T5TllLuqapqYLzHrKDCFLESDgAwe5zObovz58w5ab7pLXfen9t+9hWjc0y3Dw7lSw8/Me524aEjR6ddOH0uAioAAMAkeK4FiqEjR8cNnjseP5R9reFc/8Kl2b2/lXaVcbcLr1g0846L6eILAADQASsWdY/bFfzhxw/l3kf2ZfvgUJb2NPMnX340G65fOabT7wffeE1ecPHxgNpuV3lwz8Hc+fXd+eJDg9n+2MG02/U6ynm6rKACAAB0wAsu7s4HXn913vWJ+06ab/rG77o0ew608oq+3tz8mhfnoT0H859/9CV56sjRPO+iebm273in3/HOut50w8qsXDo/179w6bTbAiygAgAAdECjUfKyFRfmxuv6066Sqkpuu3tHnjh0JI2S0Y6/R45W2fRX949ptjQSPLcPDo2G0+T4FuEPffb+3Hhdf/ovnn79T2zxBQAA6JAVi7qzallPfvOuh/KRzz2QJw4dyU03rMx3XXZhqir562/uyTd27c9FF5yX5HgA3bh5a7YPDiU59bzVdpXsOdCa8n+fs2UFFQAAoENGuv1e+fZX5luDQ2l2zcnF87vy0N6n8sO/etdJW3937jseSPccaKWvtztHj1XjNlA6cQV2OhFQAQAAOuybew6ObtXdcMMVufXzD500fuatr+zPRz73QJpdjSxZ0MzDjw/lK/+4L+9/3VX59uOHsnnLI6MrsCuXzk9f7/Tr8iugAgAAPEO7XWX74FB2729lac/JM0zPpWeeI21XGXfbbikZPYO64qIL8sf3/mM+9NnvnE197z9dnYUXnJeLu8/LS1dcNO0aJCUCKgAAwBjjdcbdtH5N1q1eNimhb7xzpONt2117xcV5w0svSV9vd7YPDo12/02OB9j3/PG23Hhdf1635pJpGU4TTZIAAADGGK8z7omNic61pT3NMfNQb7/nkdx0w9i5p5vWr8nL+xalf/H8NBrllM2Rrly6YFpu7R1hBRUAAOAEpwp/ew60JmVsS19vdzatXzMaip84dCQrl87Pn75jbfYebGXJgpO3GI+E2meusr5oWc+0XT1NBFQAAIAxThX+Jqsr7kgn31Ub1mbPgbGB9PIl4wfiZ4bakVXWF1w8fVdPk6RUVdXpGsYYGBiotmzZ0ukyAACAWWqqz6CeqZFGTs8MtXVXSrmnqqqBcR8TUDtnKjuDAdOfewYATJ3pGv6mg2cLqLb4dsh0+VYGqAf3DACYWo1GSf/i+ZNy5pRT08W3Q6a6MxgwvblnAACzgYDaIafqDLZ7f6tDFQF19mzdBAEAZgoBtUOeOesoOd4ZbPhYlXa7XueCgc471T1jsroJAgB0goDaIX293fngG68ZM3x3w/Urc/On7rNlDzjJSCv5Zw7sns6DuAEAnkmTpA5pNEqed2Ezb31lf0pJqiq57e4d2bmvNWkDgIHp69nmowEAzBQC6jky0fEP7XaV8+Y0MqeRtKvkj/7hkezc1xrdstduV3n48aHs3n84Q0eO5rJF3XnBxT6MwmymmyAAMNMJqBM0XhBNMqHxD+ONi9hw/crc+fVd2XDDC7NrXys79z2VB/cczAf+7OtGSgAAALOCgDoBp5pD+OLlC8Yd/7Bqw9pxVzq2Dw7lg3d8bXR7b5Lc+fVd+Wff3Zcbb9sy+to33bAyF11wXnbuaz3nawIAAEx3miRNwKnmEO7ef3jc8Q+PDx3OQ3sP5m8ffCwP7T042p13cOhw3jywIh/9wkP58J0P5Dfveig/+8rL8/OfuG/Ma3/os/fnDS+7dMxrGikBAADMVFZQJ+BUcwgPHTmaDTdckZHpMLff80jOm1vy6JOt/ORH/+6kLbrnNRq55c77x4TRr+/aP+5rlxN28xopAQAAzGQC6gSMzCE8MUg2uxqZ02jk1s8/NGZr7tWXLszbfmfLuNt+Hz905KQw2q4y7muPHDc1UgIAAJjpbPGdgPHmEH7wjdfk5k+dvDX3yHB73BXRHYND6Wl2jb7GiD/58qP5wOuvHvPa/+YHrsy8rjnZcMMV+di/uFaDJACAGaLdrsY9CgaznRXUCRhvDuHg0OHsGHxqzPNaw+10N+eOuyL6pW8/mSsWz8+7fmjVmA69P/byFfmuyy7Mn75jbb62a3++uftAfvt/bc8Th47kg2+8Jkfa7WwfHDL3EABgmjtV402LESCgTth4cwjHC6JLF5yfTevXnDRK5ra7d+SJQ0fyOz/78vzKj74kQ4ePprs5Nwuac/L8i46Hzxdc3J0XL+/J9/T3ZvhYlZs/dV92DD7l5gUAMAOcqvGmaQ0goJ6RE2ehLlnQzId/4qV5++9/acw3YCsWdWfFou70vuUVueuBx1JVyW1378jOfa0sX9jMtx9v5d2fvG/M74wYCcFJ8upb7nLzAgCYQU7VeHPPgZbPeMx6AuoEnWpLxh03rc2upwPridtwFy84P79510NjbkJvGrh0NJwmpw6ebl4AADPPqRpvmtYAmiRN2Km2ZLSr5Nr+i9O/eP6Y7bfjNVbq6+0eN3ju3j92xumSBc2Tmik1uxpZPN/NCwBguhrv86FpDXCcFdQJGhw6nLe+sn90Punt9zySnftap1zVHGms9MJ3rM3Djw9l7pxGcoqRMhecN2fM785pJDfdsDIf+uz9Y0bYzPG1AgDAtDVe402NMOE4AXUC2u0q//hkKx/9wkNjGh99bMvDmdc1J+12lUajjDmjesF5c9Ou2nn48afyztvvTWu4nV94zYuy4fqVueXO+8e8zvCxsauqO/e18rt/u2M0EFdV8rt/uyMvXXFh+i62xRcAYLoar/EmIKBOyPbBodGQmRzflnvLnfdn0/o12fAHX8rNr3lxLr94fr707Sfzrk98pwHSza95cW79/INpDbezfGEzyxbOyy/d8bUxwfNjWx7OuquWJflOE6ZGKXni0JF85HMPjNbgfAIAADBT2Sw6AadqWvTAnoM5crTK/bsP5hNbHx0NpyOPv+/TX81rrrkkSfKGl12aX7rja3nzwIp89AsP5fZ7HsmcRvJvX/XCVFVy9Gg7d2zblVffclf+9ce25qYbVjqfAAAAzApWUCfgVB3XDh9t5w0vuzQf+uz9edva/nFD7Mi50VKSHYNP5ba7d+SmG1amZ15X3vfpr46utn7wjddk019+I63h9ugW3xuv689Ln39hLuvtdj4BAACYsQTUCRjpuHbiiJkN16/MbXfvyBu/69LRYDpeiF21rGfMSujOfa0cPHxstAFScjzIvvP2e/P2778iTw23Rxsx/eGWR/K9l/c6owAAAMxoAuoEnNhxbff+VoaPVbn5U/dl575W5pTjwfP2ex45qQHSza95cX7rCw/m1p8ayLzzGlm5ZH7eefu9KSXjrrYuW9jMuz/5lTGde5f1OHcKAADMbALqBJ3Yca3drvLffuYV2XOglWU9zVzW2513feK+3Hb38W25Ky66ILv2t/K8hefnl390zej23Ha7ytWXLMzeg4fzm3c9dNJq68OPHxqzqvqhz96fV714Waf+lQEAAKaEgDoBR4+2s23nvgwOHU5P87wcOXp8tfMVfb2j50JvvK4/7So51k7+n7/8Zp44dCSf2bB2zPbckZA73pbhD7z+6vznP//GmOu2htvZe7CVy5fY4gsAAMxcAuppOnq0nccO7s+xY1UGDw7n//rvXxoTKl+24sJceuEFWbWsZ0zgfLauu+MNaW6U5IlDR8Y8z2gZAABgNihVVU3uBUo5P8nPJnlVVVWvf67nDwwMVFu2bJnUms7EriefzOfv359Hnnwqt37+5G25N17Xn1XLevKqFy3Nw08cGg2cE+26225XuWPbrpNC7rrVy3TvBQAApr1Syj1VVQ2M99hUrKB+I8mXkiyYgmtNmocfP5b3/PG2U46RaVfJxs1bR7fznmnH3fFWVY2WAQAAZoPGFFxjTZIPTcF1JtXuA4fHjJE5UbOrkao6HlT3HGid9bVGzqhe239x+hfPF04BAIBZYdIDalVVTz7Xc0opN5ZStpRStuzdu3eySzojS3vOHzNG5sSZphuuX5k/+odHnBUFAAA4C7VoklRV1a1Jbk2On0HtcDnjWnHRnLz3n16V9/zxV3Lb3Tty0w0r87wL5+XBvQdz29078sShI8/aEAkAAIBnV4uAOh1c2Jyf61Ymv/uWV2T3gVaWLmhmxaI5efGynnzv5b3OigIAAJwlAfU0NZtzc2Hm59tlX0pKSjkeWpddODcrp3f/JwAAgFoQUCeg2Zybl7+gt9NlAAAAzEhTElCrqvrrJH89FdcCAABgepqKMTMAAADwnARUAAAAakFABQAAoBYEVAAAAGpBQAUAAKAWBFQAAABqQUAFAACgFgRUAAAAakFABQAAoBYEVAAAAGpBQAUAAKAWBFQAAABqQUAFAACgFgRUAAAAakFABQAAoBYEVAAAAGqhVFXV6RrGKKXsTbLjNJ9+cZLHJrEcZifvKyaL9xaTwfuKyeK9xWTwviJJLquqavF4D9QuoE5EKWVLVVUDna6DmcX7isnivcVk8L5isnhvMRm8r3gutvgCAABQCwIqAAAAtTDdA+qtnS6AGcn7isnivcVk8L5isnhvMRm8r3hW0/oMKgAAADPHdF9BBQAAYIYQUAEAAKiFaRlQSynrSynfKqU8UEr52U7Xw8xRStn29PvqgVLKb3W6HqavUsr5pZR/WUr5xDN+flMp5eFSyjdKKT/UqfqYnp7lfbXvhHvXeztVH9NTKeW8UsqvlVK+WUq5v5Tyxqd/7n7FWXmW95Z7Fqc07c6gllIWJPlqkmuTHEuyNcnVVVXt7WRdzAyllAeqqrqi03Uw/ZVStif5UpIFVVX9wNM/uzzJnyX5riTPT/JXOT6oerhTdTK9nOJ9dX6SLVVVXd3J2pi+SinLkryyqqqPl1KuTPJ3Sb47yZ/E/YqzcIr31tK4Z/EspuMK6g8m+Zuqqh6tqmpXkjuT3NDhmpg5ptc3NtTZmiQfesbPXp9kc1VVB6qq+mqS7Tn+4Q9O15qc/L7qTfLE1JfCTFFV1a6qqj7+9D9/M8nRJD8W9yvO0ineW8vinsWzmI4B9flJdpzw50eSLO9QLcwgpZTuJEtLKQ+VUj5XSnl5p2ti+qqq6slxfuz+xVk5xfvqwiSrSykPllI+XUqxC4QzVkp5S5J7kyyK+xXn0Anvre64Z/EspmNAPS9J+4Q/t3N8qy+claqqhqqq6qmqqj/JryX5xHP9DkyQ+xfnXFVVX62qqjfJyiSfS/I7HS6JaaqU8u+SbEjyz+J+xTl04nvLPYvnMh0D6s4kl5zw50uTfLtDtTBDVVX1h0mapZQLO10LM4r7F5Omqqp2kl9PsrrTtTD9lFI+kmRVku+rqmpn3K84R8Z5byVxz+LUpmNA/fMkP1hKWfL0wevvTfIXHa6JGaCUsrCU0vv0P/9QksdPsZ0OztSfJvmxUsoFpZQX5fgWuq2dLYnprpSy9OkjCknykznehAROWynl2iQvrKrqZ6qqOvT0j92vOGvjvbfcs3gucztdwERVVbW7lPLvk/zt0z/6t1VVDXWyJmaMRUn+qpSSJLuSvKmz5TDTVFV1Tynl95JsS9JK8rZqurVSp476k/xBKeVokgeS/IsO18P0sybJQCnlgRN+9vYk7lecrTU5+b310ST/p3sWpzLtxswAAAAwM03HLb4AAADMQAIqAAAAtSCgAgAAUAsCKgAAALUgoAIAAFALAioAAAC1IKACAABQCwIqAAAAtSCgAgAAUAsCKgB0QCnlF0opv/r0P/9MKeW2TtcEAJ1WqqrqdA0AMOuUUuYl+XKSH0zyqSSvqqpqV2erAoDOsoIKAB1QVdVTSd6T5M4kvy2cAoCACgCddDjJ/CTDnS4EAOrAFl8A6IBSyvwkW5K8McmfJLm2qqo9na0KADrLCioAdMb7k2yuqmpbkv+S5IMdrgcAOs4KKgAAALVgBRUAAIBaEFABAACoBQEVAACAWhBQAQAAqAUBFQAAgFoQUAEAAKgFARUAAIBaEFABAACoBQEVAACAWvj/Af1h+5fh8BTNAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(\n",
    "    x=data['x'],\n",
    "    y=data['y'],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b12d1d3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "\n",
    "#设置DBSCAN聚类参数\n",
    "eps = 0.5\n",
    "min_samples = 5\n",
    "\n",
    "dbscan = DBSCAN(eps=eps, min_samples=min_samples)\n",
    "\n",
    "target = dbscan.fit_predict(\n",
    "    data[['x', 'y']]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "94a85d39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='x', ylabel='y'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_clusters = target.max() + 1\n",
    "\n",
    "sns.scatterplot(\n",
    "    x = data['x'],\n",
    "    y = data['y'],\n",
    "    hue=target,\n",
    "    palette=sns.color_palette()[:n_clusters+1],\n",
    "    s=100,\n",
    "    style=target,\n",
    "    markers=['X'] + ['o']*n_clusters\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56b5b81a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='x', ylabel='y'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "n_clusters = 4\n",
    "\n",
    "kmAlgo = KMeans(n_clusters)\n",
    "kmModel = kmAlgo.fit(data[['x', 'y']])\n",
    "\n",
    "target = kmModel.predict(data[['x', 'y']])\n",
    "\n",
    "sns.scatterplot(\n",
    "    x='x',\n",
    "    y='y',\n",
    "    hue=target,\n",
    "    data=data,\n",
    "    palette=sns.color_palette()[:n_clusters],\n",
    "    s=100\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21f866ae",
   "metadata": {},
   "source": [
    "# 思考\n",
    "\n",
    "1. 如何评判聚类分析的准确性？\n",
    "2. 以上聚类分析的有效性如何？\n",
    "3. 如何提升以上聚类分析的效果？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca598ded",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
